Spectral Graph Features for Learned Heuristics in Automated Planning

A common approach in learning for automated planning is to train a domain-dependent heuristic on small problems and apply it to larger ones. Recent work showed that 1-Weisfeiler–Leman (1-WL) can extract effective features from planning states, outperforming deep-learning approaches while reducing training time to seconds. This result motivates the study of alternative graph-based features. Spectral graph methods are a natural candidate because they are well studied and can capture global structure that finite-round 1-WL may miss. We present the first study of spectral features for learning heuristic functions in classical planning. Our results show that spectral features outperform 1-WL in multiple domains.